Week 3 Worksheet
Learning outcomes
By the end of the session, you should be familiar with:
- how to create and interpret cross-tabulations (contingency tables)
- how to create some of the most commonly used plots to visually summarise relationships between two or more variables
- the basic intuition behind “associations” among variables
Intro
We will continue where we left off last week, completing any exercises that remained unfinished. Then, using the same datasets that we downloaded last week (EVS7, ESS10), we tabulate or plot (as appropriate for the given data type) the relationship between the “social trust” variable and some other variables. We will then use a country-level dataset (available here) to reproduce the association between inequality and social trust presented in Figure 4.1 in Wilkinson and Pickett (2010).
Exercise 0: Your module data and analysis folder
In Week 2 you created a folder for this module on your institutional OneDrive storage drive (e.g. C:\OneDrive - Newcastle University\SOC2069) and within that a sub-folder called “Data”. You used that sub-folder to save the datasets downloaded as part of the exercises in the Week 2 Worksheet. If you haven’t go back to the Week 2 Worksheet and follow the guidance there to set up your folder structure for future use.
Exercise 1: Contingency tables
About 20 minutes
In Week 2 Worksheet - Exercise 3 we explored univariate distributions (i.e. single variables). As part of that exercise, you used country-specific data from the “World Values Survey, Wave 7”, which you downloaded directly from the WVS website. You may also have saved your work (and modified dataset) from Week 2 as a .jaspwith the name “wvs7-example.jasp” (or anything else that you found useful) as instructed in Week 2 Worksheet - Exercise 3 - Point 8. If so, you can open that file for this exercise and continue where you left off. Otherwise, follow the instructions in Week 2 Worksheet - Exercise 1 and 3 to download the WVS dataset (if needed) and load it into JASP.
As part of Week 2 Worksheet - Exercise 3 you have probably created a frequency table of the “social trust” variable. In this exercise, you will check how those frequencies are distributed across the levels of another categorical variables. For this purpose contingency tables are a useful tool.
- Open the “WVS7” dataset. You should now see something like this:
- Using the survey questionnaire, identify the following two variables in the dataset: “social trust” and “sex”. In the original dataset these should be named
Q57andQ260, respectively (however, you may have already renamed the “social trust” variable as part of last week’s exercise). Using the Descriptives > Descriptive Statistics menu option in JASP, create frequency tables for these two variables. You can request frequency tables for your variables in the Tables sub-option:
Questions
- What percentage of the respondents in your dataset had answered that “Most people can be trusted”?
- What is the percentage of female respondents in your dataset?
- What are the variables’ measurement level (Column type)? Is that correct?
If the two variables were recorded as “Scale” in your dataset, then it is useful to change the column type (measurement level) to “Categorical”, which is the correct variable type for these two variables. We can do this by clicking on the Edit Data menu tab and scrolling horizontally to the variable of interest, then clicking on the data type icon next to the variable name and selecting the appropriate type for that variable; e.g.:
Once you change the variable type, you will notice that the value labels appear in the frequency tables instead of their numeric values, making it easier to understand.
If the variables were recognised as “Ordinal” by the software, then the labels are also identified and used in outputs, just like with “Nominal” variables.
As a next step, we are interested in finding out the distribution of social trust among men and women. For this, we will create a contingency table. Use the Frequencies > [Classical] Contingeny Tables menu option. Move the “social trust” variable to the Rows field and the “sex” variable to the Columns field.
Interpret the Contingency Tables part of the output (you can hide the Chi-Squared Tests table form the output by un-ticking the \(\chi^2\) option under the Statistics sub-menu). Expand the Cells sub-menu and experiment with the options.
Questions
- How many women in your dataset answered “Need to be very careful?
- What percentage of the men in your dataset answered that “Most people can be trusted”?
- What percentage of those who answered “Most people can be trusted” are men, and what percentage are women?
- Based on your cross-tabulation, do women or men in your dataset have a higher level of “social trust”
- Now find a few other “Nominal” or “Ordinal” variables in the dataset and add them to the analysis. Repeat point 4 above with other combinations of variables. What happens if you add a third variable to Layers?
Exercise 2:
About 20 minutes
Exercise 3:
About 30 minutes
Exercise 4: Begin your analysis for Assignment 1!
Below are some research questions that you can choose from to address in Assignment 1:
- Are religious people more satisfied with life?
- Are older people more likely to see the death penalty as justifiable?
- What factors are associated with opinions about future European Union enlargement among Europeans?
- Is higher internet use associated with stronger anti-immigrant sentiments?
- How does victimisation relate to trust in the police?
- What factors are associated with belief in life after death?
- Are government/public sector employees more inclined to perceive higher levels of corruption than those working in the private sector?
For now, choose one question that you find most sympathetic (you don’t need to stick with it for the assignment, but you could if you wanted to!). All of the questions can be answered with at least one of the survey datasets that you downloaded (the “WVS7” or “ESS10”) and often they both contain relevant variables.
Identify your “explanandum” - i.e. the core phenomenon/concept/behaviour/etc. that the research question aims to explain. The questions all postulate a relationship/association between two or more variables (the topic of next week), but for now, think carefully about the question and how it is formulated, and identify which is the variable that will be the target of explanation, and which variable (if mentioned) will be used for explaining it. For example, in the research question “Does education increase social trust?”, the variable we are interested in explaining is “social trust”, while “education” is the variable that we will use to explain it. In later weeks we will develop better vocabulary to describe associations between variables.
Once the core phenomenon to be explained is identified, look through the two survey questionnaires to identify any variables that might exist in the dataset that captures it. This may require some trial-and-error with testing out search words.
Once you have found one (or several) candidate variable(s), navigate to the relevant survey website and select a single country for which to download data. You will be working with single-country datasets for your assignment. Download the dataset, import it into JASP, find the relevant variable and perform some descriptive analysis on the chosen variable as you have done in the previous exercise.
Make sure to add your noted and interpretations on the analysis results and save your analysis for later. You could create a new sub-folder for your “Assignment 1” work and save your analysis there for future use. If you end up liking your chosen question, you can continue this analysis next week.